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In machine learning decision trees are widely used techniques thanks to both their interpretability and good accuracy performance. A decision tree has a flowchart-like structure where each branch node represents a "test" on attributes and each leaf node represents a decision taken after computing all attributes. They can be used both for classification and regression tasks. Since designing optimal decision trees is NP-hard, classical methods are characterised by greedy approaches. Given the substantial progress in mixed-integer linear programming (MILP) and nonlinear programming, optimal decision trees have recently attracted renewed attention. In my project I’m interested in optimization methods for Sparse Randomized Decision Trees for both classification and regression tasks.